Some simple examples

No Stata talk would be complete without the auto dataset.

. sysuse auto
(1978 Automobile Data)

We can make a better term for energy usage

. ** have: distance per energy; want: energy per distance
. gen gp100m = 100/mpg
. label var gp100m "Gallons per 100 miles"

We can do regressions...

. regress gp100m weight c.displacement##foreign

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  4,    69) =   64.16
       Model |  94.2376637     4  23.5594159           Prob > F      =  0.0000
    Residual |  25.3385971    69  .367226045           R-squared     =  0.7881
-------------+------------------------------           Adj R-squared =  0.7758
       Total |  119.576261    73  1.63803097           Root MSE      =  .60599

------------------------------------------------------------------------------
      gp100m |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   .0012254   .0002216     5.53   0.000     .0007832    .0016676
displacement |   .0029178   .0018215     1.60   0.114    -.0007159    .0065516
             |
     foreign |
   Domestic  |          0  (base)
    Foreign  |  -1.161456    .708529    -1.64   0.106    -2.574933    .2520208
             |
     foreign#|
          c. |
displacement |
    Foreign  |   .0156473    .005816     2.69   0.009     .0040447      .02725
             |
       _cons |   .5714418   .4522969     1.26   0.211    -.3308659     1.47375
------------------------------------------------------------------------------

And we can put results directly into the running text. For instance, we can see that the the coefficient for weight is 0.00123 and that it has a p-value of 0.0000005.

Embedding graphs requires using a piece of free software (ImageMagick). This will need some massaging to make it easy enough for everyone to use.